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Backscattering measurements from individual Scots pine needles.

Sanna Kaasalainen1, Miina Rautiainen

  • 1Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, P.O. Box 15, FI-02431 Masala, Finland. sanna.kaasalainen@fgi.fi

Applied Optics
|August 7, 2007
PubMed
Summary

Ground measurements reveal unique hot spot signatures for conifer needles. This research provides essential data for multiangular remote sensing and forest reflectance modeling.

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Area of Science:

  • Remote Sensing
  • Forestry
  • Optics

Background:

  • Developing multiangular remote sensing instruments requires reliable ground reference data.
  • Existing data on conifer needle spectral properties for forest modeling are scarce due to measurement challenges.
  • Directional spectral properties of needles are currently unavailable, even at single wavelengths.

Purpose of the Study:

  • To present ground reference measurements of directional scattering properties of conifer needles.
  • To address the scarcity of data for forest reflectance modeling and physically based model inversion.
  • To provide unique data on the hot spot signatures of individual conifer needles.

Main Methods:

  • Monochromatic backscattering measurements of Scots pine needles were conducted in a controlled laboratory setting.

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  • A specialized instrument was used for backscatter measurement at 1,064 nm.
  • Techniques common in laser backscattering and CCD photometry were adapted for needle measurements.

    • Main Results:

    • Strong backscattering peaks were observed for conifer needles near 0 degrees.
    • The amplitude of the backscattering brightening reached up to approximately 40%.
    • Unique hot spot signatures of individual conifer needles were documented.

    Conclusions:

    • The study provides novel ground reference data on conifer needle directional scattering.
    • The findings are crucial for advancing multiangular remote sensing and forest reflectance modeling.
    • The unique hot spot signature data can improve the accuracy of physically based forest models.